In proceedings of International Conference on Computer Graphics, Visualization and Computer Vision, 2017

Abstract

We propose a novel example-based approach for road network synthesis relying on Generative Adversarial Networks (GANs), a recently introduced deep learning technique. In a pre-processing step, we first convert a given representation of a road network patch into a binary image where pixel intensities encode the presence or absence of streets. We then train a GAN that is able to automatically synthesize a multitude of arbitrary sized street networks that faithfully reproduce the style of the original patch. In a post-processing step, we extract a graph-based representation from the generated images. In contrast to other methods, our approach does neither require domain-specific expert knowledge, nor is it restricted to a limited number of street network templates. We demonstrate the general feasibility of our approach by synthesizing street networks of largely varying style and evaluate the results in terms of visual similarity as well as statistical similarity based on road network similarity measures.

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Bibtex

@INPROCEEDINGS{hartmann-2017-StreetGAN,
author = {Hartmann, Stefan and Weinmann, Michael and Wessel, Raoul and Klein, Reinhard},
title = {StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks},
journal = {International Conference on Computer Graphics,},
booktitle = {International Conference on Computer Graphics, Visualization and Computer Vision},
year = {2017},
abstract = {We propose a novel example-based approach for road network synthesis relying on Generative
Adversarial Networks (GANs), a recently introduced deep learning technique. In a pre-processing
step, we first convert a given representation of a road network patch into a binary image where
pixel intensities encode the presence or absence of streets. We then train a GAN that is able to
automatically synthesize a multitude of arbitrary sized street networks that faithfully reproduce
the style of the original patch. In a post-processing step, we extract a graph-based representation
from the generated images. In contrast to other methods, our approach does neither require
domain-specific expert knowledge, nor is it restricted to a limited number of street network
templates. We demonstrate the general feasibility of our approach by synthesizing street networks of
largely varying style and evaluate the results in terms of visual similarity as well as statistical
similarity based on road network similarity measures.}
}